29 research outputs found

    The Epidermal Growth Factor Receptor (EGFR) Promotes Uptake of Influenza A Viruses (IAV) into Host Cells

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    Influenza A viruses (IAV) bind to sialic-acids at cellular surfaces and enter cells by using endocytotic routes. There is evidence that this process does not occur constitutively but requires induction of specific cellular signals, including activation of PI3K that promotes virus internalization. This implies engagement of cellular signaling receptors during viral entry. Here, we present first indications for an interplay of IAV with receptor tyrosine kinases (RTKs). As representative RTK family-members the epidermal growth factor receptor (EGFR) and the c-Met receptor were studied. Modulation of expression or activity of both RTKs resulted in altered uptake of IAV, showing that these receptors transmit entry relevant signals upon virus binding. More detailed studies on EGFR function revealed that virus binding lead to clustering of lipid-rafts, suggesting that multivalent binding of IAV to cells induces a signaling platform leading to activation of EGFR and other RTKs that in turn facilitates IAV uptake

    Human Rights in Child Protection Rights-Based Practice and Marginalized Children in Child Protection Work

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    Our point of departure in this chapter is to ask whether the avowed aim of a preventative approach in child protection, with strategies that set out to avoid the very large moral and economic costs of placement outside the family, is at all well served by the prevailing distribution of child protection assistance to families and children. And how might rights-based, professional child protection work be of help? The chapter starts with a discussion of marginalization as a prevailing empirical characteristic used to describe families in contact with child protection services (CPS). After this, the focus shifts to a discussion of the role implementation of CRC can play, with the right to education (Articles 28, 29) as a concrete focus.publishedVersion© The Author(s) 2018. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/

    How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

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    Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice
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